Hearing system and method for operating a hearing system

ABSTRACT

The method for operating a hearing system comprising at least one hearing device; at least one signal processing unit; at least one user control by means of which at least one audio processing parameter of said signal processing unit is adjustable; and a sensor unit; comprises the steps of
     a) obtaining adjustment data (userCorr) representative of adjustments of said at least one parameter carried out by operating said at least one user control;   b) obtaining characterizing data (p 1 ;p 2 ) from data outputted from said sensor unit substantially at the time said adjustment data are obtained;   c) deriving correction data (learntCorr) from said adjustment data (userCorr); wherein step c) is carried out in dependence of said characterizing data; and   d) recognizing an update event; and, upon step d):   e) using corrected settings for said at least one audio processing parameter in said signal processing unit, which corrected settings are derived in dependence of said correction data (learntCorr).   

     An improved automatic adaptation of the audio processing properties of the hearing system the hearing system user&#39;s preference can be achieved.

TECHNICAL FIELD

The invention relates to the field of hearing systems and hearingdevices. It relates to methods and apparatuses according to the openingclause of the claims. In particular, it relates to the adapting of audioprocessing properties of hearing devices and hearing systems to thepreferences of a user, which is also known as “fitting” of hearingdevices/hearing systems.

Under a hearing device, a device is understood, which is worn in oradjacent to an individual's ear with the object to improve theindividual's acoustical perception. Such improvement may also be barringacoustic signals from being perceived in the sense of hearing protectionfor the individual. If the hearing device is tailored so as to improvethe perception of a hearing impaired individual towards hearingperception of a “standard” individual, then we speak of a hearing-aiddevice. With respect to the application area, a hearing device may beapplied behind the ear, in the ear, completely in the ear canal or maybe implanted.

A hearing system comprises at least one hearing device. In case that ahearing system comprises at least one additional device, all devices ofthe hearing system are operationally connectable within the hearingsystem.

Typically, said additional devices such as another hearing device, aremote control or a remote microphone, are meant to be worn or carriedby said individual.

Under audio signals we understand electrical signals, analogue and/ordigital, which represent sound.

BACKGROUND OF THE INVENTION

It is common in hearing devices that the user of a hearing device canadjust audio processing parameters such as parameters influencing thevolume or the tonal balance, possibly even the compression, thebeam-former setting, bass, treble or noise suppression. Usually, suchadjustments are temporary, i.e. when switching off the hearing device,the adjustments are “forgotten”, i.e. reset to default values (defaultparameter settings). When a hearing device uses a classifier forclassifying a current acoustic environment and selecting audioprocessing parameters in dependence of such a classification, thebefore-mentioned adjustments may even be “forgotten” as soon as theacoustic environment changes.

In a conventional procedure for optimizing the adaptation of the audioprocessing properties of a hearing device to the preferences of theuser, the user will verbally report his preferences to his hearingdevice professional (audiologist, fitter) during a fitting session, andthe hearing device professional will change the default parametersettings accordingly. This can be a rather cumbersome procedure.

From U.S. Pat. No. 5,604,812, a hearing device is known, which employsfuzzy logic or neural network technology in order to let the hearingdevice automatically calculate improved audio processing parametersettings. Such algorithms require large processing power and dosometimes provide unreliable results.

In US 2005/0129262 A1, a programmable auditory prosthesis with trainableautomatic adaptation to acoustic conditions is disclosed.

US 2006/0215860 A1 discloses a hearing device and a method for choosinga program in a multi program hearing device.

US 2004/0208331 A1 discloses a device and a method to adjust a hearingdevice. The method comprises: inputting a desired setting value in thehearing device at a determinable point in time; measuring at least onesound quantity concerning a first environment situation at thedeterminable point in time; automatically learning setting values to beused, depending on the desired setting value and the at least onemeasured sound quantity; newly measuring at least one sound quantityconcerning a second environment situation; and adjusting the hearingdevice to one of the setting values to be used with regard to the secondenvironment situation.

In US 2006/0222194 A1 is disclosed a hearing aid for recording data andlearning therefrom.

From EP 0 788 290 A1, a programmable hearing aid-device is known. It isdisclosed to analyze audio signals in the frequency domain and to usethe result of such an analysis for selecting stored parameters of anamplification and transmission member or for changing the amplificationand transmission characteristics of the amplification and transmissionmember.

In EP 1 404 152 A2, a hearing-aid device is presented, which isadaptable to certain hearing situations. A continuous individualadaptation of the hearing-aid device in different hearing situations isachieved.

It is desirable to provide an alternative way of adapting the audioprocessing properties of a hearing system to the preferences of a userof the hearing system.

SUMMARY OF THE INVENTION

One object of the invention is to create an alternative way of adaptingthe audio processing properties of a hearing system to the preferencesof a user of the hearing system; in particular a way that does not havethe disadvantages of the method and devices of the state of the artmentioned above. A method for operating a hearing system shall beprovided, and, in addition, a corresponding hearing system and acorresponding computer program product shall be provided.

One object of the invention is to provide a way to fit a hearing system,which produces reliable results.

One object of the invention is to provide a way to fit a hearing system,which does not require a lot of storage space.

One object of the invention is to provide a way to fit a hearing system,which does not require large computing power.

One object of the invention is to provide a way to fit a hearing system,which works (predominantly) autonomously.

Further objects emerge from the description and embodiments below.

At least one of these objects is at least partially achieved byapparatuses and methods according to the patent claims.

The method for operating a hearing system comprising

-   -   at least one hearing device;    -   at least one signal processing unit;    -   at least one user control by means of which at least one audio        processing parameter of said signal processing unit is        adjustable;    -   a sensor unit;        comprises the steps of

-   a) obtaining adjustment data representative of adjustments of said    at least one parameter carried out by operating said at least one    user control;

-   b) obtaining characterizing data from data outputted from said    sensor unit substantially at the time said adjustment data are    obtained;

-   c) deriving correction data from said adjustment data;    wherein step c) is carried out in dependence of said characterizing    data; and

-   d) recognizing an update event; and,    upon step d):

-   e) using corrected settings for said at least one audio processing    parameter in said signal processing unit, which corrected settings    are derived in dependence of said correction data.

In one aspect, said method for operating a hearing system can beconsidered a method for adjusting a hearing system, in particular thesound processing properties of a hearing system, to the preference of auser of the hearing system.

The hearing system comprises

-   -   at least one hearing device;    -   at least one signal processing unit;    -   a user interface comprising at least one user control by means        of which at least one audio processing parameter of said signal        processing unit is adjustable;    -   a sensor unit;    -   a control unit operationally connected to each of the above        elements;        wherein said control unit is adapted to

-   a) obtaining adjustment data representative of adjustments of said    at least one parameter carried out by operating said at least one    user control;

-   b) obtaining characterizing data from data outputted from said    sensor unit substantially at the time said adjustment data are    obtained;

-   c) deriving correction data from said adjustment data;    wherein step c) is carried out in dependence of said characterizing    data; and

-   d) recognizing an update event; and,    upon step d):

-   e) using corrected settings for said at least one audio processing    parameter in said signal processing unit, which corrected settings    are derived in dependence of said correction data.

The computer program product comprises program code for causing acomputer to perform the steps of

-   A) obtaining adjustment data representative of adjustments of at    least one audio processing parameter of a signal processing unit of    a hearing system carried out by operating at least one user control    of said hearing system;-   B) obtaining characterizing data from data outputted from a sensor    unit of said hearing system substantially at the time said    adjustment data are obtained;-   C) deriving correction data from said adjustment data;    wherein step c) is carried out in dependence of said characterizing    data; and-   D) recognizing an update event; and,    upon step d):-   E) using corrected settings for said at least one audio processing    parameter in said signal processing unit, which corrected settings    are derived in dependence of said correction data.

In one embodiment, said computer is comprised in said hearing system.

The computer-readable medium comprises a computer program productaccording to the invention.

Through this, an improved adaptation of the signal processing propertiesof the hearing system to the preferences of a user of the hearing systemcan be achieved.

The steps of a method according to the invention may take place in saidhearing device or elsewhere in the hearing system; they may, inparticular, be partially carried out in said hearing device andpartially in one or more other devices of the hearing system.

The members of a hearing system according to the invention may becomprised in said hearing device or maybe distributed among one or moredevices of the hearing system including or excluding the hearing device.

For example, said signal processing unit is typically comprised in saidhearing device. Said user interface can be comprised in said hearingdevice and/or in a remote control comprised in the hearing system.

Said operating said at least one user control mentioned in step a) istypically carried out by a user of the hearing system.

Said update event can be, e.g., a start-up of said hearing system or ofsaid hearing device, or a particular operation of said user interface.

In one embodiment, a time-dependent function is used for carrying outstep c). In other words, step c) comprises using a time-dependentfunction; step c) is carried out in a time-dependent fashion. Forexample, said time-dependent function can describe a time-integration,more particularly a time-dependent time integration over substantiallysaid adjustment data. Preferably, in said time-dependent function ortime integration, more recent adjustment data are weighted stronger thanadjustment data which occurred a longer time ago.

In one embodiment, step c) is carried out such that said correction datadevelop in time towards said adjustment data.

Preferably, said correction data evolve towards said adjustment data ina preferably gradual fashion.

In one embodiment, said time-dependent function is a recursive function.In said recursive function, it is possible to obtain new correction datafrom recent correction data and current adjustment data. For example, acorrection data value at a time t2 can be derived as a functiondepending on a correction data value at a time t1 before t2 and on anadjustment data value at t2. In a more mathematical formulation:learntCorr(t2)=f(learntCorr(t1),userCorr(t2)),with

-   -   f: a function,    -   learntCorr: correction data,    -   userCorr: adjustment data.

The function may further depend on t1 and/or t2, in particular on thetime difference t1-t2.

The points in time at which new correction data are obtained can bepre-determined, in particular be substantially regularly spaced. It isalso possible that these points in time are determined in anevent-driven fashion, in the sense that new correction data are obtained(step c)), e.g., also or only when new adjustment data are obtained(step a)).

In one embodiment, step c) is carried out several times after eachother, wherein the result of later-obtained correction data depends onbefore-obtained correction data.

In an important embodiment, step c) is carried out during normaloperation of the hearing system. I.e. step c) does not have to becarried out offline; it is carried out while the hearing system useruses his hearing system. Note that corrected settings (which depend oncorrection data) are not used before an update event occurred.

Data logging is known in the state of the art. By data logging, datasuch as the adjustment data mentioned above are recorded in the hearingsystem. See, e.g., EP 1 414 271 A2 for details on data logging inhearing devices. This allows a thorough evaluation of the recorded databy a hearing device professional, typically after recording data forseveral days or weeks, which requires a considerable amount of storagespace. Data logging can, of course, be used in conjunction with thepresent invention, too. But when, as described above, a time-dependentfunction is used for deriving correction data (step c)), continuouslyimproved correction data can be obtained without the need to store largeamounts of adjustment data.

As has been pointed out, step c) is carried out in dependence of saidcharacterizing data. I.e. (newly) obtained correction data will dependon the characterizing data, and in particular, it is possible to adjustthe amount to which the adjustment data contribute to (newly) obtainedcorrection data in dependence of the characterizing data.

In one embodiment, said time-dependent function describes a weightedaveraging function.

The use of a weighted averaging function can have the advantage thatvalues/events of the more distant past contribute less to the resultthan more recent values/events.

In one embodiment, said sensor unit receives sound. In particular, saidsensor unit receives sound from the acoustic environment of a user ofsaid hearing system. In other words, said characerizing data can becharacteristic for said received sound and, more particularly, for theacoustic environment said user is located in.

In one embodiment, said characterizing data comprise data characterizingacoustical properties of said received sound. Such properties can be,e.g., the sound pressure level, the shape of the frequency spectrum.

In one embodiment, said sensor unit comprises a classifying unit forclassifying said received sound according to N sound classes, with aninteger N≧2.

Typically, four classes, sometimes three or five or six, possibly evenmore classes are used. Classification of sound is well known in the artof hearing devices. It is used for choosing an appropriate set of audioprocessing parameters for processing sound in a hearing device dependingon the acoustic environment the user is in.

Note that, as depicted above, classification is here not necessarilyused for choosing an appropriate set of audio processing parameters forprocessing sound in a hearing device, but for deriving correction data.It is possible that in a hearing device or hearing system, both iscarried out. But it is also possible that classification is not used foradjusting currently used audio processing parameters, while neverthelessclassification is used for deriving correction data. And it is alsopossible that in the same hearing device or hearing system,classification is carried out for both above-stated purposes, but with(at least partially) different classes according to which theclassifications are carried out.

In one embodiment, said characterizing data comprise similarity factorswhich are indicative of the similarity between said received sound andsound representative of a respective class.

In one embodiment, the method comprises the step of

-   g) deriving, on the basis of input audio signals derived from said    received sound and for each class of N classes each of which    describes a predetermined acoustic environment, a class similarity    factor indicative of the similarity of a current acoustic    environment as represented by said received sound with the    predetermined acoustic environment described by the respective    class, wherein N is an integer with N≧2.

In one embodiment, said hearing system comprises a storage unitcomprising at least one set of base parameter settings for each of saidclasses, wherein said correction data are derived for each of saidclasses, and wherein for each of said classes, corrected settings arederived in dependence of the correction data and of said base parametersettings of the respective class.

Typically, for each adjustable (and enabled) parameter and for eachclass, corrected settings are obtained in the following manner:corrected settings=function(base parameter settings,correction data),or more particularly for example:corrected settings=base parameter settings+correction data.

It is possible to provide configuration steps in the invention. Forexample, it is possible to allow to select (enable) those audioprocessing parameters (and/or corresponding user controls), for whichadjustment data and/or correction data shall be obtained (calculated).And it is possible to provide that said time-dependent function isselectable, in particular values which influence the “speed of learning”such as the speed with which said correction data converge towards saidadjustment data. And it is possible to provide that said classes can beselected or determined.

Such configuration issues will typically be handled by a hearing deviceprofessional such as an audiologist or acoustician.

In one embodiment, said hearing system is identical with said hearingdevice.

Of course, several of the embodiments described above can be combinedwith each other (pair-wise or more).

Note that the invention comprises hearing systems and computer programproducts with features of corresponding methods according to theinvention, and vice versa.

The advantages of the methods correspond to the advantages ofcorresponding apparatuses.

Further embodiments and advantages emerge from the dependent claims andthe figures.

BRIEF DESCRIPTION OF THE DRAWINGS

Below, the invention is described in more detail by means of examplesand the included drawings. The figures show:

FIG. 1 a block diagrammatical illustration of a hearing system;

FIG. 2 a schematical curve graph for illustrating the various variablesinvolved in learning;

FIG. 3 a schematic diagram illustrating how correction data can beapplied to a set of base parameter settings;

FIG. 4 a schematic diagrammatical illustration of how an interpolatedparameter set can be obtained in a hearing system with “mixed-mode”classification;

FIG. 5 a schematical curve graph illustrating an embodiment, in whichlearning is only active in a class if the similarity factor of thatclass is above a threshold;

FIG. 6 an illustration of a weight function as a function of asimilarity factor;

FIG. 7 an illustration of a weight function as a function of asimilarity factor;

FIG. 8 a schematical curve graph for illustrating the various variablesinvolved in learning.

The reference symbols used in the figures and their meaning aresummarized in the list of reference symbols. The described embodimentsare meant as examples and shall not confine the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows a block diagrammatical illustration of a hearing system 1.The hearing system 1 can be identical to a hearing device 10 or cancomprise a hearing device and one or more further devices.

The hearing system 1 comprises an input unit 102 such as a microphone, asignal processing unit 103 such as a digital signal processor and anoutput unit 105 such as a loudspeaker.

The hearing system 1 comprises furthermore a sensor unit 104 such as aclassifier, a control unit 108 such as a processor, an interface unit106 such as an interface to fitting hardware and software, a userinterface 110 comprising user controls such as switches 111,112, and twostorage units 107 and 109.

During normal operation of the hearing system 1, sound (sound waves)referred to as incoming sound 5, typically originating in the acousticenvironment in which a user of the hearing system 1 is located, areconverted into audio signals by input unit 102. These audio signals arefed into signal processing unit 103, and the processed audio signals areconverted by output unit 105 into signals to be perceived by the hearingsystem user, typically sound. The audio processing properties of signalprocessing unit 103 are adaptable by adjustable audio processingparameters so as to allow to adapt the processing to the needs of thehearing system user.

The audio signals outputted by input unit 102 are also fed, afteroptional processing, as audio signals S1 into sensor unit 104. Sensorunit 104 will output characterizing data which characterize a magnitudesensed by sensor unit 104, e.g., the acoustic environment as representedby audio signals S1. If sensor unit 104 comprises a classifier whichclassifies the (current) acoustic environment according to N classes(N≧2), each class representing a base class such as “pure speech”,“speech in noise”, “noise”, “music” or the like, said characterizingdata can comprise a similarity vector p1, . . . , pN comprising onesimilarity factor (or similarity value) for each of said N classes,wherein such a similarity factor is indicative of the similarity(likeness) between the sensed (current) acoustic environment and therespective base class. Preferably, the similarity factors are normalizedsuch that the sum of the similarity factors of all classes is 1 (or100%).

In storage unit 107, there will be (at least) one set of base parametersfor each of said N classes. Based on these sets of base parameters,audio processing parameters to be used in processing unit 103 can bechosen in dependence of the similarity vector. This is controlled bycontrol unit 108.

Accordingly, the hearing system 1 can automatically adapt its signalprocessing properties in dependence of the current acoustic environment.Nevertheless, it is possible that the user is not always content withthe signals he is presented with. In order for the user to carry outadjustments by himself whenever he feels a need to do so, there isprovided user interface 110, e.g., with user controls 111,112 foradjusting the overall output volume and further user controls such asfor adjusting the high frequency content of the output signals of thehearing system 1. Operating a user control such as 110 or 111, will leadto the generation of adjustment data (indicated as “userCorr”), whichare fed to control unit 108 so that the corresponding audio processingparameter(s) is/are adjusted, usually with immediate effect.

The invention is closely related to ways of “learning” from adjustmentsthe user carries out, in particular “learning” in the sense of findingbetter audio processing parameter settings, such as improved sets ofbase parameter settings.

Storage unit 109 is used for the learning and can also be used for datalogging or, more concretely, for storing the adjustment data (userCorr).As will become clear, it is possible to dispense with storing largeamounts of adjustment data, because it is possible to determine improvedparameter settings “on the run”, i.e. during normal operation of thehearing system 1, so that an online evaluation of the adjustment data(userCorr) takes place, which allows to delete adjustment data alreadyafter a short time.

In the following, the invention will be discussed in detail by furtherfigures, wherein it will partially be referred to FIG. 1, too.

FIG. 2 is a schematical curve graph for illustrating the variousvariables involved in learning. The bold solid lines indicate theadjustment data userCorr, whereas the dotted lines indicate correctiondata learntCorr obtained from the adjustment data. The audio processingparameter dealt with in FIG. 2 can be, e.g., the overall output level(in dB).

In the beginning, a default value as given by the appropriate baseparameter settings is used. After three hours, the user increases thevolume by 8 dB, i.e. the adjustment data userCorr will amount to +8 dB.According to a time-dependent function, the correction data learntCorrwill gradually and monotonously develop towards the userCorr value of +8dB. Another three hours later, the user switches off his hearing system.Up to that time, the hearing system 1 “learnt” about 50% of theuserCorr, corresponding to a learntCorr of about +4 dB.

The switching-on of the hearing system is used as an update event, whichmeans that the so-far learnt correction data (learntCorr=+4 dB) are usedas an offset (also referred to as base parameter offset) for the defaultparameter settings given by the base parameter settings. Accordingly,when switching on the hearing system again, an initial setting of thevolume will be about 4 dB increased with respect to the setting used atthe last switching-on. I.e. userCorr=+4 dB. And learntCorr=+4 dB.

After three hours, the user again perceives the signals provided by thehearing system as too soft and increase the volume (using user control111) again, this time by 5 dB, thus selecting userCorr=+9 dB. Again,learntCorr will slowly develop towards the new userCorr and this timewill reach userCorr.

Several hours later, the user decrease the volume by 15 dB such thatuserCorr=−6 dB, and learntCorr will follow userCorr again.

In a similar fashion, the learning of other adjustable audio processingparameters is possible.

FIG. 8 is a schematical curve graph for illustrating the variousvariables involved in learning, which is similar to FIG. 2. Itillustrates a different time-dependent function according to whichlearntCorr evolves towards userCorr.

FIG. 3 shows a schematic diagram illustrating how correction data can beapplied to a set of base parameter settings. When the hearing system isused for the first time after a fitting session, initially the baseparameter settings as set by the hearing device professional will beactive. Then, the user uses the hearing system and adjusts parameters(cf. also FIGS. 2 and 8), i.e. he applies corrections (userCorr) tothese parameters, and the hearing system will learn from theseadjustments (learntCorr; cf. also FIGS. 2 and 8). I.e. correction dataare generated.

When the device is switched off and back on again, this can beconsidered an update event, the learnt correction (learntCorr) is addedas an offset to the base parameter settings. It is possible toprovide—as indicated by the dotted arrow labelled reset—that the usercan decide that the new settings used after the restart of the hearingsystem (original settings plus learntCorr as offset) shall not befurther used, i.e. it can be returned to the original settings if theuser prefers to do so.

During the next fitting session with the hearing device professional(follow-up fit), the offset can be added to the base parameters (or usedotherwise for amending them) so as to result in corrected settings,which serve as new base parameter settings. It is also possible toprovide that the hearing device professional can amend the settingsresulting from the original settings and the correction data, asindicated by the dotted portion of the corrected base parametersettings.

It is to be noted that, upon an update event, it is possible to directlyderive corrected setting, without the intermediate steps of usinglearntCorr as an offset and involving the hearing device professional.In FIG. 3, this is indicated by the dashed arrow labelled “update*”. Themain—and rather unimportant—difference between such a procedure and theprocedure implied by FIGS. 2, 8 and 3 is where the zero-reference linefor userCorr and learntCorr is located (cf. FIGS. 2 and 8). In FIGS. 2and 8, the zero line would coincide with the thin dashed line used forindicating the base parameter offset. And the base parameter offsetwould indicate the difference between the original (old) base parametersettings and the new base parameter settings (corrected settings).

It is advantageous to provide a copy of (original) base parametersettings as set by the hearing device professional, because in that way,the hearing device professional can easily see which changes have takenplace. This can, nevertheless also be achieved by storing the originalsettings at the hearing device professional's place (where plenty ofstorage space is easily available, unlike in a hearing system, inparticular in a hearing device). In the first-described effect of anupdate event, the original settings are automatically still stored inthe hearing system.

FIG. 4 shows a schematic diagrammatical illustration of how aninterpolated parameter set can be obtained in a hearing system with“mixed-mode” classification. In what is referred to as mixed-modeclassification, base parameter settings are mixed in dependence of theoutput of a sensor unit 104 for obtaining interpolated parametersettings.

We shall assume for this example that sensor unit 104 is a classifier.In a given situation, the classifier for N=3 classes outputs similarityfactors as indicated in FIG. 4, i.e. the similarity of the currentacoustic environment with each of the three base classes is p1=70%,p2=20% and p3=10%, respectively. Each class has base parameter settings,and the parameter settings to be used in signal processor 103 isobtained as a function of these base parameter settings and thesimilarity values. E.g., these interpolated parameter settings can beobtained as a linear combination of the base parameter settings of theclasses. As indicated by the dashed lines, the base parameter settingsof the classes as shown in FIG. 4 can be understood to be composed oforiginal base parameter settings and an offset, wherein the offset islearnt.

Confer also above the discussion of the updating in conjunction withFIGS. 2, 8 and 3.

If the user did adjust at least one audio processing parameter, asindicated by the dotted lines, the parameters used in signal processingunit 103 will be composed of said interpolated parameter settings andthe user adjustments (userCorr).

For the purpose of learning, it can be very valuable to separatelyprovide correction data (learntCorr) for different classes.

It can be very valuable if, for the purpose of learning, the “learningspeed” depends on characterizing data such as the similarity factors.For example, it can be useful to leave correction data (learntCorr)unchanged for such classes which have a very low similarity factor.

Formula (1) describes a weighted averaging function. This formula can beused for the above-mentioned time-dependent function according to whichlearntCorr evolves towards userCorr.learntCorr_(—) i _((t))=(1−weight_(—) i)*learntCorr_(—) i_((t−1))+weight_(—) i*userCorr  (1)Therein,i=1, . . . , N; N is number of classest: time variable, time-dependent indexweight_i: weight factor; weight_iε[0;1]

The learning speed, which determines, how fast learntCorr evolvestowards userCorr, is basically determined by the weight factor. Theweight factor for a class i advantageously depends on the similarityfactor of class i. For example, it can be defined by Formula 2:

$\begin{matrix}{{weight\_ i} = {\frac{1}{\tau}*{f_{p\_ i}({p\_ i})}}} & (2)\end{matrix}$Therein:τ: time constant; parameter determining general “learning speed” (hetime constants are typically between 1 hrs and 4 days, and more likelybetween 8 hrs and 36 hours.)fp_i(p_i): similarity-dependent function

Note that pi means the same as p_i, namely the similarity factor ofclass i.

More generally, the similarity-dependent function can be fp_i(p1, . . ., pN), i.e. it can depend also on the similarity factors of otherclasses.

FIG. 5 shows a schematical curve graph illustrating an embodiment, inwhich learning is only active in a class if the similarity factor ofthat class is above a threshold. The similarity-dependent functiondescribing the learning behaviour in FIG. 5 can be described by Formula(4):

$\begin{matrix}{{f_{p\_ i}\left( {p\_ i} \right)} = \left\{ \begin{matrix}1 & {for} & {{p\_ i} > {{p\_ i}{\_ threshold}}} \\0 & {for} & {{p\_ i} \leq {{p\_ i}{\_ threshold}}}\end{matrix} \right.} & (4)\end{matrix}$

I.e., below the similarity threshold, no learning takes place of therespective class, and above the threshold, learning takes place, at alearning speed as given by time constant τ. The similarity thresholdscan be identical or different for different classes. Preferred valuesfor threshold are between 0.5 and 0.7 (at similarity factors normalizedto 1).

Referring to the top portion of FIG. 5, the user carries out anadjustment of an audio processing parameter at time tA, and he undoesthe adjustment at time tB. In the middle portion of FIG. 5, datareferring to class 1 are shown, in particular the evolution of classsimilarity factor p1 with time (obviously, the acoustic environmentchanges with time) and the correction data learntCorr1 for class 1 as afunction of time. In the lower portion, the situation for class 2 isshown in a similar manner.

At t1, p1 exceeds the threshold: learning can begin. Since no adjustmenthas been carried out, learntCorr remains zero. At tA, the useradjustment is carried out, and learntCorr1 develops towards the currentuserCorr value. From t2 on, learntCorr1 remains unchanged, because p1drops below the threshold.

At t3, p2 exceeds the threshold, and learning can begin for class 2:learntCorr2 rises towards userCorr. When at tB, userCorr drops,learntCorr2 follows userCorr again. At t4 finally, p2 drops below thethreshold, so learning stops and learntCorr stays constant.

It is also possible to provide that a certain degree of learning takesplace for all classes, even for classes that have a similarity factor ofzero. An exemplary similarity-dependent function is shown in Formula(3):f _(p) _(—) _(i)(p _(—) i)=[p _(—) i*α+(1−α)];αε[0;1]  (3)

By means of α, it can be adjusted, how strongly the learning speed for aclass shall be influenced by the respective class. If thesimilarity-dependent function is defined like that for all classes (andwith the same α), learning is purely “global” in that not only userCorr,but also the learning speed (as given by the weight factor) is the samefor all classes. At α=0, there is always maximum learning, independentof p_i, whereas at α=1, learning is directly proportional to p_i.

It is possible to provide that α and/or τ are adjustable, typically by ahearing device professional. For example, they can be adjusted such thatlearning speed is relatively high during the time of acclimatization andlower at later times.

FIG. 6 is an illustration of a weight function as a function of asimilarity factor. The corresponding function is given in Formula (5):

$\begin{matrix}{{f_{p\_ i}\left( {p\_ i} \right)} = \left\{ \begin{matrix}{p\_ i} & {for} & {{p\_ i} > {p{\_ threshold}}} \\0 & {for} & {{p\_ i} \leq {p{\_ threshold}}}\end{matrix} \right.} & (5)\end{matrix}$

In this embodiment, learning is enabled only above a threshold (compareFormula (4)), but the learning speed depends on the similarity factor ofthe respective class. It is, in this example, directly proportional tothe similarity factor.

FIG. 7 is an illustration of another weight function as a function of asimilarity factor. In this case, the learning speed increases step-wisefrom no learning up to a similarity factor of 0.5, to 50% of the maximumlearning speed for 0.5<p<0.75, to full learning speed (1/τ) above asimilarity factor of 0.75.

It is also possible to combine aspects of the Formulae (4) and (3),e.g., as shown in Formula (6):

$\begin{matrix}{{f_{p\_ i}\left( {p\_ i} \right)} = \left\{ {\begin{matrix}\left\lbrack {{{p\_ i}*\alpha} + \left( {1 - \alpha} \right)} \right\rbrack & {for} & {{p\; 1} > {p{\_ threshold}}} \\0 & {for} & {{p\; 1} \leq {p{\_ threshold}}}\end{matrix};\mspace{14mu}{\alpha \in \left\lbrack {0;1} \right\rbrack}} \right.} & (6)\end{matrix}$

As will have become clear, there are various possibilities to definesimilarity-dependent functions, many of which have not been explicitlymentioned, but they all have in common that the learning speed (theweight factor) depends on at least one similarity factor, which is veryvaluable to have, since it can increase the quality of the learnedcorrections.

The variability of the user input can be taken into consideration todefine the learning speed. The higher the variability the lower thelearning speed and vice versa.

Please note that for the sophisticated learning put forward in theabove, it is not necessary, that the parameter settings actually used inthe signal processing unit 103 are determined using a classifier. And,even, if a classifier is used for that, it is possible to use“fixed-mode” classification for that, which means that the baseparameter settings of that one class are used, which has the largestsimilarity factor (no mixing/interpolating of base parameter sets ofdifferent classes).

It is possible to either provide more than one set of base parametersettings per class, each for different times of the day and/or fordifferent days of the week or for different sound pressure levels orother, typically acoustic parameters, or to provide a correspondinglyincreased amount of classes. This can help to better adjust the hearingsystem to the user's preferences, and the above-sketched procedures canbe carried out analogously in these cases.

By means of the invention, an increased stability of the learning can beachieved, and resulting corrected settings are likely to correspondclosely to settings the hearing system user really prefers. Theinvention enables an improved self-adjusting hearing system. Theself-adjusting to the user's preferences depends, in a sophisticatedway, on audio processing parameter adjustments the user himself carriesout.

LIST OF REFERENCE SYMBOLS

-   1 hearing system-   5 incoming sound-   10 hearing device-   102 input unit, input transducer unit, microphone unit-   103 signal processing unit, signal processor, digital signal    processor-   104 sensor unit, classifying unit, classifier-   106 interface unit, interface to fitting hardware-   107 storage unit-   108 control unit-   109 storage unit-   110 user interface-   111 user control-   112 user control-   learntCorr correction data-   p1, . . . , pN similarity factors (for classes 1 . . . N)-   p1_threshold similarity threshold for class 1-   p2_threshold similarity threshold for class 2-   S1 input audio signals-   userCorr adjustment data

What is claimed is:
 1. A method for operating a hearing systemcomprising at least one hearing device; at least one signal processingunit; at least one user control by means of which at least one audioprocessing parameter of said signal processing unit is adjustable; asensor unit; said method comprising the steps of a) obtaining adjustmentdata representative of adjustments of said at least one parametercarried out by operating said at least one user control; b) obtainingcharacterizing data from data outputted from said sensor unitsubstantially at the time said adjustment data are obtained; c)determining an amount of contribution of said adjustment data based onsaid characterizing data; d) deriving correction data from saidadjustment data based on the determined amount of contribution ofadjustment data; e) recognizing an update event; and, upon step e): f)using corrected settings for said at least one audio processingparameter in said signal processing unit, which corrected settings arederived in dependence of said correction data.
 2. The method accordingto claim 1, wherein a time-dependent function is used for carrying outstep d).
 3. The method according to claim 2, wherein step d) is carriedout such that said correction data develop in time towards saidadjustment data.
 4. The method according to claim 2, wherein saidtime-dependent function is a recursive function.
 5. The method accordingto claim 2, wherein said time-dependent function describes a weightedaveraging function.
 6. The method according to claim 1, wherein saidsensor unit receives sound.
 7. The method according to claim 6, whereinsaid characterizing data comprise data characterizing acousticalproperties of said received sound.
 8. The method according to claim 6,wherein said sensor unit comprises a classifying unit for classifyingsaid received sound according to N sound classes, with an integer N≧2.9. The method according to claim 8, wherein said characterizing datacomprise similarity factors which are indicative of the similaritybetween said received sound and sound representative of a respectiveclass.
 10. The method according to claim 8, wherein said hearing systemcomprises a storage unit comprising at least one set of base parametersettings for each of said N≧2 classes, and wherein said correction dataare derived for each of said N≧2 classes, and wherein for each of saidN≧2 classes, corrected settings are derived in dependence of thecorrection data and of said base parameter settings of the respectiveclass.
 11. A hearing system comprising at least one hearing device; atleast one signal processing unit; a user interface comprising at leastone user control by means of which at least one audio processingparameter of said signal processing unit is adjustable; a sensor unit; acontrol unit operationally connected to each of the above elements;wherein said control unit is adapted to a) obtaining adjustment datarepresentative of adjustments of said at least one parameter carried outby operating said at least one user control; b) obtaining characterizingdata from data outputted from said sensor unit substantially at the timesaid adjustment data are obtained; c) determining an amount ofcontribution of said adjustment data based on said characterizing datad) deriving correction data from said adjustment data based on thedetermined amount of contribution of adjustment data; e) recognizing anupdate event; and, upon step e): f) using corrected settings for said atleast one audio processing parameter in said signal processing unit,which corrected settings are derived in dependence of said correctiondata.
 12. A non-transitory computer-readable storage medium havingstored thereon computer program products comprising program codes forcausing a computer to perform the steps of A) obtaining adjustment datarepresentative of adjustments of at least one audio processing parameterof a signal processing unit of a hearing system carried out by operatingat least one user control of said hearing system; B) obtainingcharacterizing data from data outputted from a sensor unit of saidhearing system substantially at the time said adjustment data areobtained; C) determining an amount of contribution of said adjustmentdata based on said characterizing data D) deriving correction data fromsaid adjustment data based on the determined amount of contribution ofadjustment data; E) recognizing an update event; and, upon step E): F)using corrected settings for said at least one audio processingparameter in said signal processing unit, which corrected settings arederived in dependence of said correction data.
 13. The non-transitorycomputer-readable storage medium according to claim 12, wherein saidcomputer is comprised in said hearing system.
 14. The non-transitorycomputer-readable storage medium according to claim 12, wherein saidsensor unit receives sound.
 15. The non-transitory computer-readablestorage medium according to claim 12, wherein said user control is partof a user interface.
 16. A method for operating a hearing systemcomprising at least one hearing device; at least one signal processingunit; at least one user control by means of which at least one audioprocessing parameter of said signal processing unit is adjustable; asensor unit; said method comprising the steps of a) obtaining adjustmentdata representative of adjustments of said at least one parametercarried out by operating said at least one user control; b) obtainingcharacterizing data from data outputted from said sensor unitsubstantially at the time said adjustment data are obtained; c)determining an amount of contribution of said adjustment data based onsaid characterizing data; d) deriving correction data from saidadjustment data based on the determined amount of contribution ofadjustment data; e) recognizing an update event; and, upon step e): f)using corrected settings for said at least one audio processingparameter in said signal processing unit, which corrected settings arederived in dependence of said correction data, wherein said sensor unitreceives sound and comprises a classifying unit for classifying saidreceived sound according to N sound classes, with an integer N≧2,wherein said hearing system comprises a storage unit comprising at leastone set of base parameter settings for each of said N≧2 classes, whereinsaid correction data are derived for each of said N≧2 classes, andwherein for each of said N≧2 classes, corrected settings are derived independence of the correction data and of said base parameter settings ofthe respective class.